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mdclust--exploratory microarray analysis by multidimensional clustering.

M Dugas1, S Merk, S Breit

  • 1Department of Medical Informatics, Marchioninistr. 15, D-81377 Munich, Germany. dug@ibe.med.uni-muenchen.de

Bioinformatics (Oxford, England)
|January 31, 2004
PubMed
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Multidimensional clustering (mdclust) identifies sample subgroups and associated genes from microarray data. This method aids in discovering gene-phenotype associations, even with numerous variables.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Unsupervised clustering of microarray data can reveal hidden sample characteristics.
  • Potential applications include identifying diagnostic subgroups with distinct gene profiles or experimental errors.

Purpose of the Study:

  • To introduce multidimensional clustering (mdclust) for analyzing microarray data.
  • To enable the identification of gene-phenotype associations.

Main Methods:

  • mdclust iteratively applies two-means clustering and score-based gene selection.
  • It identifies sets of sample clusters and associated genes.
  • Phenotype variables are used to select the best matching cluster sets, with an optional discriminant step for gene reduction.

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Main Results:

  • mdclust successfully identifies sample clusters and associated genes.
  • The method facilitates the discovery of gene-phenotype associations.
  • It is effective even with a large number of phenotype variables.

Conclusions:

  • mdclust is a valuable tool for exploring complex microarray datasets.
  • It enhances the ability to uncover biologically relevant patterns and associations.